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Pre-publication version

Accepted at the Journal of International Management

on 10 May 2019

Diverse effects of diversity:

Disaggregating effects of diversity

in global virtual teams

Vas Taras, Daniel Baack, Dan Caprar, Douglas Dow,

Fabian Froese, Alfredo Jimenez, Peter Magnusson

ABSTRACT

Global Virtual Team (GVT) member diversity provides many advantages but also poses many

challenges. Diversity comes in different forms that each have different effects on GVT

dynamics and performance. Past research typically explored the effect of only one type of

diversity at a time. Using multi-source, multi-wave data from 5,728 individuals working in 804

consulting project GVTs, the present study is unique in that it explores and compares the effects

of different forms of team member diversity on different aspects of GVT effectiveness in a

single sample. It proposes a refined theoretical model that differentiates between the effects of

personal versus contextual diversity and articulates how these distinct forms of diversity affect

different aspects of GVT effectiveness (i.e., task outcomes versus psychological outcomes).

The results reveal that (1) team member diversity in general has a substantial effect on GVT

effectiveness; (2) contextual diversity has a positive effect on task outcomes; and (3) personal

diversity has a negative effect on psychological outcomes. Implications for practice and future

research are discussed.

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1. INTRODUCTION

Technological development and greater global integration have led to an unprecedented

rise of global virtual collaboration in the workplace (Chudoba, Wynn, Lu, & Watson-Manheim,

2005; Martins, Gibson, & Maynard, 2004). Experts estimate that approximately 60% of

managers regularly complete tasks as members of geographically dispersed virtual teams

(Hertel, Geister, & Kondradt, 2005) and, more recently, that “corporate teams are now almost

entirely virtual” (RW3 CultureWizard, 2016, p. 3). Due to an accelerated rate of

internationalization coupled with the development of online collaboration technologies,

organizations are increasingly employing global virtual teams (GVTs).

A GVT have been defined as a “temporary, culturally diverse, geographically

dispersed, and electronically communicating work group” (Jarvenpaa & Leidner, 1999, p. 792)

or as a team whose members are separated by time and space, and differ in national, cultural,

and linguistic attributes, and whose functioning is heavily dependent on computer-mediated

technologies (Zakaria, 2009). GVTs offer several potential advantages. First, by removing the

need for and costs associated with being physically present in a certain location, teams can be

staffed with experts from around the globe. Second, drawing on the diversity of perspectives

and resources brought in by their members, GVTs have the potential for increased creativity

and enhanced problem solving. Third, the “sun never sets” in GVTs and the workload can be

handed off seamlessly from one time zone to the next (e.g., Duckworth, 2008). However,

despite many potential benefits, GVTs also face substantial obstacles. Differences in language,

culture, and institutional environments come with increased coordination costs and

information-processing losses, ultimately limiting team effectiveness (Maznevski & Chudoba,

2000; Montoya-Weiss, Massey, & Song, 2001).

This mix of inherent advantages and obstacles prompts the following question: Is GVT

diversity “good” or “bad” for team effectiveness? To date, “research on the relationship

between work group diversity and performance has yielded inconsistent results” (van

Knippenberg, De Dreu & Homan, 2004, p. 1008). Efforts to integrate the team diversity

literature have been made (e.g., Stahl, Maznevski, Voight, & Jonsen, 2010); yet, the extant

literature does not provide definitive answers or clear guidance to managers. This is

problematic given the increased use of GVTs by many organizations, and a strong desire by

managers to understand how GVT performance can be optimized (Jiménez, Boehe, Taras, &

Caprar, 2017).

Earlier research on the effects of diversity in teams is subject to several notable

limitations. First, the operationalization of diversity has typically been limited to simple

‘surface-level’ categories, such as gender, age, race and/or citizenship (e.g., Baugh & Graen,

1997; Ely, 2004; Harrison, Price, & Bell, 1998), while less visible types of differences tended

to be overlooked. Second, prior research has often been based on in-classroom simulations

where teams completed relatively simple and short activities. The time for these was often

limited from just a few minutes to a few hours (e.g., Cox, Lobel, & McLeod, 1991; Watson,

Kumar, & Michaelson, 1993). Such experiments tend to be a poor reflection of organizational

management and reward structures. Third, some studies have simulated cultural differences

and geographic dispersion through scenario playing, or priming, which could have masked true

effects (Oyserman, Coon, & Kemmelmeier, 2002). Fourth, team effectiveness is a complex

construct, and team member diversity may affect each of its different facets in separate ways

(e.g., Bleijenbergh et al. 2010; Milliken & Martins, 1996; Webber & Donahue, 2001). Fifth,

prior studies tended to focus on a specific single type of diversity, which made it impossible to

compare the effects of different types of diversity, spare a few very rare attempts when such

comparisons were made by the means of a meta-analysis (e.g., Stahl et al, 2010).

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This study aims to overcome these limitations by doing the following: (1)

simultaneously examining and comparing a more comprehensive set of different types of

diversity, (2) using a large sample of GVTs (5,728 participants working in 804 GVTs) that

worked together for a substantial period of time (on average 2 months), and (3) considering the

effects of diversity on both supervisor-rated team performance (task outcomes) and personal

evaluation of satisfaction (psychological outcomes). Furthermore, based on our review of the

GVT literature, we develop an integrated theoretical model that explains when the effects of

team diversity are expected to be positive, negative, or non-significant. Thus, our conceptual

model and empirical analysis provide a framework to think about the effects of different types

of diversity on different aspects of team effectiveness and offer actionable advice for

practitioners.

We purposely cast a wide net of diversity indicators to achieve a richer understanding

of how different types of diversity affect team effectiveness. There are many types of team

member diversity, and it seemed necessary and useful to test their comparative effects in a

single sample. This approach is supported by, for example, Hambrick (2007, p. 1346), who

argued that “papers that identify compelling empirical patterns that cry out for future research

and theorizing” are valuable, and by Cantwell, Dunning, and Lundan (2010, p. 573) who

suggest this approach can serve as an “analytical bridge between empirical investigation and

formal models.”

We proceed with a review of the literature on the effects of GVT diversity on team

effectiveness. Next, we propose a theoretical model that differentiates between personal and

contextual diversity, and also distinguishes between teams’ outputs, such as the quality of the

product produced by the team, and psychological outcomes, such as team member satisfaction.

We then test our theoretical model and review the results which lead to new insights regarding

the impact of diversity on GVT effectiveness. We conclude with a discussion of the

implications of our findings, limitations, and suggestions for future research.

2. LITERATURE REVIEW AND THEORY BUILDING

The extant literature conflicts regarding the relationship between team member

diversity and team effectiveness. Some studies found a negative effect (Earley & Mosakowski,

2000; Thomas, Ravlin, & Wallace, 1996), while others found the opposite (for reviews see

Horwitz & Horwitz, 2007; Kirkman & Shapiro, 2005). Several literature reviews have

addressed the issue of the conflicting conclusions about the effects of team diversity, but they

focused on the effects of top management team diversity (Nielsen, 2010), board diversity

(McMahon, 2010), or the demographic diversity of the employees in general (Williams &

O’Reilly, 1998), and their effects on firm performance (Robertson, Holmes, & Perry, 2017).

The goal of the present study is to explore whether the conflicting evidence is due to the failure

of earlier research to recognize that different types of diversity affect team effectiveness

differently, particularly in the context of GVTs.

By definition, a GVT is subject to multiple types of team-member differences, which

may include demographic, cultural, geographic, economic and institutional variables

(Townsend, DeMarie, & Hendrickson, 1998). We propose that different types of team member

diversity may have different effects on different aspects of team effectiveness. Specifically,

with respect to diversity, our model differentiates between personal diversity among the team

members and the diversity of contexts that the team members represent. With respect to team

effectiveness, our model differentiates between quality of the task performance and

psychological outcomes.

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We define personal diversity as differences pertaining to the personal characteristics or

perceptions of the team members. These include diversity based on age, gender, language

skills, technical skills, personal values, and cultural intelligence. In contrast, contextual

diversity is defined as differences in the characteristics of the contexts and environments that

the team members represent, have access to, or come from. This includes diversity based on

economic development, human development, income inequality, corruption, importance of

religion, etc.

The distinction between personal versus contextual diversity echoes the earlier

differentiation made between surface-level and deep-level diversity by Stahl et al. (2010).

However, some important differences merit highlighting. First, Stahl et al. (2010) focused

specifically on cultural diversity and its manifestation at the visible (surface) versus invisible

(deep) levels. We advance the discussion by examining a broader range of inter-member

diversity in teams. Diversity is not only cultural, but it also includes demographics and various

characteristics of team members’ national environments and contexts they represent,

understand, or can utilize. We go beyond visible and invisible characteristics of the person and,

differentiate between characteristics of the person and characteristics of the contexts the team

members bring to the team. Based on their meta-analysis, Stahl et al. (2010) concluded that

cultural diversity leads to process losses through task conflict and decreased social integration,

but also leads to process gains through increased creativity and satisfaction. To better explain

this conclusion, they refined their model by differentiating between intra-national versus cross-

national diversity dimensions, hypothesizing that cross-national diversity will have a positive

effect on team creativity and a negative effect on team communication effectiveness. Their

theoretical conceptualization was not fully supported by their data. More specifically, their

results revealed no differences in performance in intra- versus cross-national teams.

Further, our model offers a more fine-grained examination of team effectiveness, as it

categorizes GVT effectiveness into two distinct categories: task outcomes and psychological

outcomes (c.f., Abbas, Raja, Darr, & Bouckenooge, 2014; de Cuyper, Castanheira, White, &

Chambel, 2014). Task outcomes are defined as the objective results of teamwork. This facet

includes such aspects as quality and innovativeness of team output, as evaluated by external

managers or supervisors. Psychological outcomes pertain to the social and psychosocial result

of teamwork, such as team member satisfaction and psychological well-being of the team

members during and after the project is completed (Hackman, 1987). The connection between

the personal and context diversity dimensions and task outcomes and psychological outcomes

of the team effectiveness can be described by a 2x2 matrix, which we present in Figure 1.

---------------- Insert Figure 1 about here --------------

In the next section, we review the theoretical arguments and empirical evidence in

support of a negative effect of diversity. An examination of the theoretical arguments and

empirical evidence supporting a positive view of diversity follows. By integrating and refining

previous theoretical contributions, we propose an integrative theoretical model for when and

why different forms of diversity may have opposite effects on different measures of GVT

effectiveness.

2.1. Negative Effects of Diversity

In the international business (IB) literature, a large body of work has focused on the

effects of distance between, for example, home and host market, buyers and sellers, JV

partners, etc. A general assumption in this literature is that distance implies barriers,

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difficulties, costs, and risks (Shenkar, Luo, & Yeheskel, 2008). Indeed, a recent review

revealed that almost 95% of the 1,141 studies that investigated distance posited negative

theoretical assumptions about its effects (Stahl & Tung, 2014). Similarly, in GVTs, diversity

presents barriers to collaboration. Separation in physical space, linguistic barriers, and cultural

differences can inhibit communication and coordination among the team members (Zakaria,

2017). This in turn undermines the team’s ability to effectively complete its tasks. Simply put,

it is more challenging to work together when you do not speak the same language, have

different values, and follow different schedules.

Two related social psychology theories predict why diversity may have negative

effects. The similarity-attraction theory postulates that people tend to associate with others

who are similar to them, both socially and professionally. Similarity on easily observable

attributes such as race, ethnicity, origin or social status, and on subtler attributes such as

attitudes and beliefs, creates real or perceived familiarity, which leads to a sense of comfort,

closeness, and efficiency in interaction (Kirkman, Rosen, Tesluk, & Gibson, 2004; Magnusson,

Schuster, Taras, 2014; O’Grady, Lane, 1996; Thomas, 1999). Similar reasoning can be made

drawing on social identity theory (Tajfel, 1974; Vahtera et al. 2017). Social identity theory

suggests that individuals depend on protection from the group. As such, people identify

themselves as members of specific groups and categorize others as members or non-members

of those groups based on a range of attributes, including visible and perceived similarities

(Haas, 2010). To show group loyalty, people tend to treat those in the in-group with favoritism

and those in the out-group with prejudice. Thus, as a consequence of the social categorization,

social identity and social comparison processes taking place within teams, diversity leads to

group fragmentation and subgroup rivalry, inhibiting communication and cooperation across

members belonging to different categories.

Putting these theories together, it can be concluded that personal homogeneity leads to

inter-member attraction, closeness, and efficiency in communication, which in turn facilitates

group integration and cohesion. In contrast, team heterogeneity has the opposite effect

(Williams & O’Reilly, 1998). Therefore, we hypothesize that:

Hypothesis 1: Personal diversity among team members will have a negative effect on

a) task outcomes and b) psychological outcomes.

Further, we expect the effects of the personal differences to relate more strongly to

psychological outcomes than to task outcomes. That is, we expect that personal diversity will

have a negative effect on both facets of team effectives, but the effect will be relatively weak

on task outcomes, and comparatively stronger on psychological outcomes. Our reasoning is an

extension of Stahl and colleagues’ (2010) model, according to which deep-level diversity aids

team creativity, whereas the effects of surface-level diversity, which fits within our concept of

personal diversity, are limited primarily to interpersonal processes, such as conflict,

communication, or satisfaction. The underlying logic is that personal diversity primarily affects

interpersonal interactions. It impedes communication, reduces attraction among the team

members, and often is associated with biases and prejudices. Thus, it has a negative effect on,

first and foremost, the psychological aspects of teamwork, such as reduced team cohesion,

satisfaction with team members and the project in general. The task outcomes may also be

adversely affected, but less proximally and to a lesser degree. Therefore, we hypothesize that:

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Hypothesis 2: Personal diversity among team members will have a stronger negative

effect on psychological outcomes than on task outcomes.

2.2. Positive Effects of Diversity

Stahl, Tun, Kostova, & Zellmer-Bruhn (2016, p. 621) recently noted that “research in

IB may have overly emphasized a negative view on distance and diversity of all kinds.” Indeed,

a growing body of research finds that team diversity can improve team performance (e.g.,

Earley & Mosakovski, 2000), in particular via increased creativity (O’Reilly, Williams, &

Barsade, 1998) and innovation (Lisak, Erez, Sui & Le, 2016). Indeed, diverse backgrounds of

team members can create value by allowing for diversity of perspectives and adaptability,

which, in turn, allows for tapping a wider range of information sources, feeding off more

networks, and thus aiding creativity and problem solving (DiStephano & Mazneski, 2000; Ng

& Tung, 1998).

The positive effects of diversity can be explained by information processing theory

(Simon, 1978) and the resource-based view (Barney, 1991). According to the information

processing theory, a large pool of information and a variety of perspectives aid problem

solving, facilitate creativity, innovation, and adaptability (Simon, 1978). Diverse team

members come from diverse contexts and thus have access to diverse knowledge. This allows

them to generate alternative and unique viewpoints, which motivates consideration of a larger

number of possibilities and solutions (William & O’Reilly, 1998). Homogeneity, in contrast,

limits the team’s ability to understand different contexts and access different pools of resources.

As a result, homogeneity may lead to “groupthink,” conformity, and narrow-mindedness

(Janis, 1982). This hampers effective decision-making, creativity, and innovation (Jackson,

1992; Nemiro, 2002; Watson, Kumar & Michaelsen, 1993).

Further, the resource-based view (RBV) suggests that the more heterogeneous the

resources, the more difficult they are to copy or substitute, and consequently the more valuable

and instrumental to team success (Janis, 1982; Shaw & Barrett-Power, 1998). Diversity

increases the chance of getting access to new learning opportunities and sources (Wernerfelt,

1984). It also broadens the pool of cognitive resources, knowledge and perspectives to which

the team has access, which aids problem solving (Cox, Label, & McLeod, 1991). Moreover,

diversity not only increases the heterogeneity of tangible and intangible resources the team

members have access to, but also allows for more ways to combine these resources, which

boosts the team’s potential (Van Knippenberg, De Dreu, & Homan, 2004). Furthermore,

diversity aids what Roberson and colleagues (2017) call “alliancing.” That is, more diverse

teams have access to more potential partners for voluntary exchange of information and

resources, as well as being better positioned to effectively manage multiple alliances and

partnerships. What makes these resources especially valuable is, unlike personal skills and

knowledge, resources associated with this kind of contextual diversity are hard to replicate.

It is also important to keep in mind that contextual diversity does not necessarily have

to follow national borders. If the team members come from different countries, but the

countries have similar institutional and cultural characteristics, then the international nature of

the team will have little effect on team effectiveness. At the same time, team members could

be from the same nation-state, but from very different socio-economic or cultural backgrounds,

in which case they will bring different perspectives and give the team access to different

knowledge and resource pools. Thus, we posit that the diversity of backgrounds of the team

members, such as the institutional, economic, religious and cultural environments they come

from leads to positive outcomes.

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Hypothesis 3: Contextual diversity will have a positive effect on a) task outcomes and

b) psychological outcomes.

Because the mechanism by which diversity exerts a positive effect is different from that

of the negative effects, these types of diversity affect different team effectiveness indicators

differently. The negative effects manifest themselves at the interpersonal level, whereas the

positive effects stem from contextual differences. At the personal level, diversity reduces

attraction, comfort, and makes communication harder and thus has a negative effect,

particularly on psychological satisfaction. At the contextual level, diversity allows team

members to tap into different pools of knowledge, connections, and resources, which in turn

increases the number of solutions the team can consider, which then improves decision making,

problem solving, and performance.

A closer inspection of the positive-effect mechanisms proposed by writings from

information processing theory (Simon, 1978) and the resource-based view of the firm (Barney,

1991) reveal that they primarily refer to the results of the team work, such as the quality of the

output the team produces or the creativity or innovativeness of their ideas. This line of

argument also aligns with Stahl and colleagues’ (2010) argument that deep-level diversity,

which fits within our contextual diversity concept, affects mainly team creativity and task

performance. This hypothesis is based on the assumption that the variety of perspectives,

knowledge, and resource pools leads to better decisions and solutions, even though the positive

effects on personal satisfaction may be less pronounced. We expect that contextual diversity

will have a positive effect on both facets of team effectiveness, but the relative effect on task

outcomes will be stronger than on the psychological outcomes.

Hypothesis 4: Contextual diversity will have a stronger positive effect on task

outcomes than on psychological outcomes.

3. METHOD

3.1. Sample

To test our hypotheses, we use data from a large multi-country collaborative project

involving 5,728 students working in 804 GVTs across 132 universities. On average, each team

had 7.1 members, with an average of 5.2 countries represented per team. The average age of

our study participants was 23.9. The average work experience in the sample was over three

years, 31% of participants were employed at the time of the study, and 5% reported owning

their own business or holding managerial positions, thus “doubling up” as corporate employees

with considerable work experience. Furthermore, 41% of the participants were MBA and

EMBA students. Aware of concerns around student samples in business research (e.g.,

editorials by Bello, Leung, Radebaugh, Tung, & Van Witteloostuijn, 2009; Wheeler, Shanine,

Leon, & Whitman, 2014), we considered the generalizability concerns. As our sample had a

substantial age range with our oldest participant being 67 years old, we tested and found that

age did not significantly correlate with any of the variables in our model. This suggests that the

younger age of our study participants does not present a major threat to the generalizability.

We designed the project task and environment to resemble the corporate world as

closely as possible. The project length was extensive, requiring 8-9 weeks of collaborative

work. Completing the project required frequent communication and close collaboration.

Further, students were randomly assigned to teams and the project involved the development

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of a solution to real-life business challenges. The task included market research, market entry

plan development, and product design. Our instructors, many with rich business consulting

experience, managed the project as if it was a regular business consulting project. Importantly,

students felt pressure to perform. On average, the project accounted for 35% of the course

grade. A failure on the project often meant a failure in the course, with all resulting potential

negative effects on future career prospects. Given these conditions, the stakes and motivation

were high and more resembled those associated with corporate project teams. This is in contrast

to the typical small payment that researchers offer to students for participating in a study (e.g.,

Brockner, De Cremer, van den Bos, & Chen, 2005; Gelfand & Christakopoulou, 1999).

3.2. Measures

The goal of the study was to simultaneously test and compare the effects of many

different indicators of diversity on team effectiveness. Accordingly, for personal diversity, we

selected demographics (age, gender), differences in skills and attitudes more relevant to work

in GVTs (communication and technical skills plus cultural intelligence), and perceived

differences (aka “psychic distance”, c.f., O’Grady, Lane, 1996). For contextual diversity we

focused on the main commonly accepted economic, political, cultural, and institutional

characteristics of each participants’ home country.

All variables used in the present study were derived from individual-level indicators

aggregated to the team level. To eliminate the threat of common method bias (Podsakoff et al.

2003), the data are drawn from multiple sources. Specifically, task outcomes were measured

by professors evaluating the outcome (report) for each team. Psychological outcomes were

measured based on participant survey responses at the conclusion of the project. Contextual

diversity was based on archival, publicly available data based on team members’ country

affiliation. Finally, personal diversity was measured based on survey responses at the beginning

or during the project. Thus, all data were drawn from different sources or at different points in

time, which should eliminate concerns about common method bias.

To capture team diversity, we used the standard deviation approach (Thomas, 1999).

For each indicator, we calculated diversity by squaring every member’s distance from the

team’s average and then taking a square root of the sum of squares.1

3.2.1. Personal Diversity

Seven personal diversity dimensions were captured in our dataset and included in the

analysis: Age diversity was the standard deviation of the team members’ ages. Gender diversity

was operationalized as gender heterogeneity in the team. A perfectly gender-homogeneous

team would be one with 100% male or female members, while a perfectly mixed team would

be 50-50% male-female. We calculated the index as 1.0 minus the absolute distance from 0.50

gender ratio. This way, the values would range from 0.50 = perfectly homogeneous (100%

male or female) to 1.0 = perfectly heterogeneous (50-50% male-female).

Each team member rated all other team members’ English proficiency with an inter-

rater reliability of 0.74. Working language skills diversity was measured as the standard

deviation of English proficiency skills among the team members.

1 An alternative method, the average team diversity method, calculates the absolute difference between

all possible team member pairs and then taking an average. As a robustness check, we tried both computations.

The two sets of indices were almost perfectly inter-correlated (0.97) and their correlations with external variables

were almost identical. Therefore, we only report the results obtained using diversity indices calculated as standard

deviations.

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Technical skills diversity was measured in the same manner. Team members evaluated

their peers in terms of their technical abilities and knowledge to use advanced online

communication and collaboration tools. The inter-rater reliability was 0.71.

Personal value diversity was measured by assessing the values of each project

participant (Dorfman & Howell 1988). The instrument consisted of 28 items, measuring

individual values pertaining to individualism, power distance, uncertainty avoidance,

achievement orientation, and gender role attitudes. The internal consistency of the instrument

ranged from 0.69 to 0.82.

Cultural intelligence diversity was measuring using the BCIQ cultural intelligence

instrument (Alon, Meyers, Boulanger, & Taras, 2016). The internal reliability of the instrument

ranged from 0.68 to 0.88. Perceived difference was measured by two items. At the start of the

project, each team member was asked to indicate the degree of perceived differences between

the countries represented on the team, and the perceived difficulty people from these countries

would have working together. The two items correlated at the 0.85 level indicating a high

internal consistency of this composite index. Importantly, perceived difference is

fundamentally different from contextual diversity. Contextual diversity deals with objective

differences of the contexts (e.g., national economic and political characteristics) that the team

members come from. Perceived difference is a subjective personal perception of how similar

or different the members of the team appear in terms of their cultural values and such.

For each of these scales, we calculated team diversity by averaging the standard

deviations of the scores for each dimension. The resulting indices were similar to that offered

by Kogut and Singh (1988), and basically indicated average absolute inter-member distance

for each corresponding contract.

3.2.2. Contextual Diversity

Whereas all of the personal diversity measures were based on team member personal

data, all of the contextual diversity measures were based on secondary data with each team

member assigned a score based on his or her home country.

National variety was measured using Groves and Feyerherm’s (2011) variety index. It

is superior to simply counting the number of nationalities represented on the team as it takes

into account the distribution of countries within the teams (i.e., even distribution or clear

majority/minority). We used the student’s nationality to operationalize this index, which

appropriately captures the national variety regardless of the country of study.

Economic and social diversity were represented as standard deviations of a series of

indicators that are typically used to describe economic and social institutions. Namely, we used

gross domestic product per capita adjusted for purchasing power parity (PPP) (GDP/capita

using PPP, World Bank data), Human Development Index (HDI, provided by the United

Nations, 2015), and Gini Index (as reported in the CIA Factbook, 2011) as indicators of

economic institutions; corruption level (Transparency International, 2015), civil freedom

(Freedom House, 2015), the percent of parliament members who are female (Inter-

Parliamentary Union, 2013), religiosity levels (Gallup, 2015) and cultural diversity (calculated

based on Hofstede’s national cultural indices from Hofstede, 2001) as indicators of socio-

cultural institutions2. All of these were operationalized as a standard deviation of the national

values generalized to the students on the team. National cultural value distance was

2 For robustness, we also assessed the cultural diversity results using indicators from Schwartz (1992)

and GLOBE data (House et al. 2004). Results were almost identical.

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operationalized as a standard deviation of the national cultural scores, as reported by Hofstede

(2001) for the team members’ country of origin.

Lastly, we measured both geographic and time-zone diversity, which where

operationalized as average inter-member distances in kilometers and hours, respectively. The

two are closely interrelated, but there are obvious differences. A GVT dispersed across Canada,

Mexico and Argentina may face a different set of challenges than a GVT with members in

Japan, France and the U.S.A.

3.2. Dependent Variables

3.2.1 Task Outcomes

Task outcomes were operationalized as the average expert rating of the team consulting

report. At the end of the project, each team’s output (consulting report) was evaluated by four

to seven professors (supervisors). Each professor evaluated the report on eight dimensions,

such as the economic feasibility and novelty of the idea, analysis quality and depth, formatting

and visual appeal, and the overall quality of the report. The internal reliability was 0.85 and the

inter-rater reliability was 0.72 to 0.91 depending on the evaluation dimension.

3.2.2 Psychological Outcomes

Psychological outcomes were measured as an average of the team members’

satisfaction with the process and project overall, satisfaction with the quality of the report

prepared by the team, and satisfaction with the team members expressed as the average of peer

evaluations. The internal consistency of this index was 0.94. It is important to note that the self-

evaluated report quality had a low correlation with the supervisor-assessed report quality (r =

0.11) indicating the former is indeed independent from the quality of the team output. Instead,

member satisfaction is largely a psychological outcome of the project. The correlation between

task outcomes and psychological outcomes was 0.24 indicating that the two are distinct

dimensions of GVT effectiveness.

3.3. Control Variables

In addition to the focal inter-member diversity variables, as noted, we controlled for

several basic team characteristics that are also expected to influence team effectiveness. This

included team size, gender composition (percent male), along with average age, prior

international experience, technical and working language skills, and cultural intelligence at

the team level.

4. RESULTS

4.1. Bivariate Correlation Analysis

For our initial set of tests, we conducted correlation analyses. The complete correlation

matrix (Table 1) suggests that not only the magnitude but also the direction of the effects vary

dramatically. Evidently, team member diversity is not just “good” or “bad” for GVTs: different

types of diversity have different effects on different dimensions of team effectiveness.

Table 2 provides a more focused overview of the bi-variate correlations between the

various types of diversity and our key outcome variables. Hypothesis 1 predicted a negative

effect between personal diversity and a) task outcomes and b) psychological outcomes, which

is largely supported. Four of the seven personal diversity dimensions are negatively related to

task outcomes and five out of seven are negatively related to psychological outcomes. None of

the personal diversity dimensions have a significant positive effect. Further, the evidence

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suggests a stronger negative relationship with psychological outcomes than with task

outcomes, which supports Hypothesis 2. Four of the seven negative correlations were

significantly stronger for psychological outcomes than for task outcomes, and only gender

diversity had a stronger negative relationship with task outcomes than with psychological

outcomes. In other words, greater diversity among team members at the personal level, at least

with respect to gender, working language, technical skills, cultural intelligence and perceived

difference, tends to hurt team effectiveness, particularly the team dynamics aspect.

-----------Insert Tables 1 and 2 about here------------

However, not all personal differences affect team effectiveness. We found non-

significant effects for age, which can perhaps be explained by the somewhat limited diversity

offered by our student sample. More importantly, personal values diversity appeared to have a

close-to-zero effect on either of the two aspects of GVT effectiveness.

In contrast, in support of Hypothesis 3, most of our contextual diversity measures had

a positive effect on the task outcomes. Six (out of eleven) dimensions had a significant, positive

relationship with task outcomes. Further, in support of Hypothesis 4, the positive effect of

contextual diversity was limited to task outcomes. For psychological outcomes, only one

dimension (civil freedom diversity) had a significant, positive relationship and one dimension

(Gini diversity) had a significant negative relationship. Also, as expected, the positive effect

was stronger for task outcomes and weaker for the psychological outcomes in about half of the

cases.

Although there appears to be support for our hypotheses, it is very important to note

that the effects of GVT diversity are not as strong and consistent as one might expect based on

the attention the issue has received in the literature. In comparison, the basic team

characteristics (controls) tend to have a stronger effect on team effectiveness than most of the

team diversity measures. The next section reports the results of more sophisticated statistical

tests of these relationships.

4.2. Multivariate Regression Analysis

To obtain more precise estimates of the partial effects of the different diversity

dimensions, while controlling for basic team characteristics, we conducted a multivariate

regression analysis. We present the regression results for psychological outcomes in Table 3

and for task outcomes in Table 4.

OLS regression assumes independence of predictors. Even moderate correlations

among independent variables can results in misleading regression indices. As per Table 1, a

number of our predictors correlated significantly with one another. Our tests for multi-

collinearity revealed only mild problems (VIF statistics in the 3.8 to 5.2 range). However, given

our lengthy list of inter-correlated predictors, the variance explained (R2) indices are of greater

interest than the actual beta-coefficients. We used step-wise regression to analyze the

cumulative effects of each block of predictors separately; that is the predictive power of

personal diversity and contextual diversity as blocks.

-----------Insert Tables 3 and 4 about here------------

12

The regression results largely corroborated the findings from the initial bivariate

correlations analysis. Several of the personal diversity indicators are negatively related to team

effectiveness and the effects are stronger for psychological outcomes than for task outcomes.

In contrast, several of the contextual diversity measures are positively related to task outcomes,

more so than the psychological outcomes.

We also examined the amount of variance explained by the diversity constructs. As

baseline, we first tested a model containing only the control variables (Models 1 and 7). Almost

all of the basic team characteristics had a significant effect and together accounted for a

substantial variance in team effectiveness, explaining 7% and 38% of the variance in task

outcomes and psychological outcomes respectively.

We then added the personal diversity variables (Models 3 and 9). Taken together, they

explained a statistically significant additional 2.5% (task outcomes) to 2.7% (psychological

outcomes) of the variance in team effectiveness. Of note, when tested without controls (Models

2 and 8) personal diversity explains 4.1% for task outcomes and a much larger 24.7% of

variance in psychological outcomes. As we expected (Hypothesis 2), the link between personal

diversity is much stronger to psychological outcomes than to task outcomes (p<0.5 for r2

difference test for Models 3 versus 9).

Next, we explored the effects of the contextual diversity block (Models 5 and 11).

Controlling for basic team characteristics and qualifications, contextual diversity explains an

additional 3.6% and 2.3% of variance in task outcomes and psychological outcomes,

respectively. Again, when tested without controls (Models 4 and 10), contextual diversity

explains 3.7% and 3.1% of variance in task outcomes and psychological outcomes. As per

Hypothesis 4, contextual diversity explains more variance in task outcomes than in

psychological outcomes, although the difference is marginally significant (p<0.10 for r2

difference test for Models 5 versus 11).

Looking at the combined predictive power (Models 6 and 12), diversity (personal and

contextual taken together) explains an additional 7.7% and 6.0% of variance in task outcomes

and psychological outcomes, respectively. Analyzed without the controls (not reported in Table

3), a combined diversity effect is 8.7% and 27.0% of variance explained in task outcomes and

psychological outcomes. Thus, the explanatory power of the diversity variables is comparable

to the explanatory power of the combined basic team characteristics and team member skills

and qualifications.

4.5. Post-Hoc Robustness Check

As a robustness check, we repeated the analyses by creating personal and contextual

diversity composite indices and used them in our analysis. Since the diversity variables in our

dataset are measured on different scales, we first standardized the data with a mean of 0 and

standard division of 1. The personal and contextual diversity composite indices were computed

as formative averages across the variables in each block (Diamantopoulos & Siguaw, 2006).

Table 5 provides correlations among the composite diversity indices and GVT outcomes.

------------------------- Insert Table 5 about here -----------------------------

We revisited our hypotheses by analyzing the relationship between the diversity types

and GVT performance measures using both bivariate correlations and multivariate regression

analysis (Tables 5 and 6). The results further corroborate our theoretical model. Based on the

bivariate correlations, personal diversity has a negative effect on both task outcomes and

13

psychological outcomes (Hypothesis 1), and the effect is stronger (p<0.01) for psychological

outcomes than for task outcomes (Hypothesis 2). The contextual diversity has a positive effect

on both measures of GVT effectiveness (Hypothesis 3), and the effect is stronger for task

outcomes (Hypothesis 4).

Multivariate regressions corroborate our results. Personal diversity has a significant

negative effect on GVT effectiveness in every model specification (Hypothesis 1). Likewise,

contextual diversity has a positive effect (Hypothesis 3) and much stronger and significant

effect for task outcomes (Hypothesis 4), though the effect is significant only if the control

variables are not used (Models 2 and 3).

Two of the eighteen constructs in our model were operationalized by the means of self-

report surveys. However, common method bias was not a serious threat because the data were

aggregated from the individual to the team level and assessed at two different time points

(Podsakoff et al. 2003). Further, the Harman’s one factor test demonstrated that no single factor

could explain the majority of variance. We also conducted a common latent factor test by

subtracting the standardized weights from a model without a common latent factor from

another model with an added common latent factor. As the highest value was .06, far below

the threshold of .20, this shows that common method bias was not a major threat to the validity

of the results.

5. DISCUSSION AND CONCLUSIONS

The research on the effects of diversity in teams has been extensive. However, as noted

before, the results have been conflicting, with some studies showing a positive effect, some

negative, and others no effect at all. The present study attempts to clarify some of this

ambiguity by simultaneously assessing and comparing the relative relationship among a

multitude of diversity and effectiveness measures in a large single sample of GVTs working

on a business consulting project. Further, we attempt to add to our theoretical understanding of

GVT effectiveness by examining two distinct categories of diversity and two different aspects

of GVT effectiveness.

The results of this study suggest that team diversity affects team effectiveness.

However, they also show that the size and the direction of the effects vary across team diversity

and effectiveness dimensions. Specifically, the data generally supported the notion that

personal diversity tends to have a negative effect, and that the effect tends to be stronger for

psychological outcomes than for task outcomes. In contrast, contextual diversity tends to have

a positive effect, particularly on task outcomes. The findings have implications for both theory

and practitioners.

5.1. Theoretical Implications

We would like to highlight three specific theoretical implications of this study. First,

the measurement of performance is an elusive construct. Reviews of the general IB literature

has found that many studies fail to consider the multi-faceted nature of performance (Hult et

al., 2008). Similarly, inconsistent conceptualization and operationalization of performance

make it difficult to synthesize findings across studies. In this study, we have explicitly

considered two distinct aspects of performance. Furthermore, the correlation between task

outcomes and psychological outcomes is rather weak, which suggests that the two facets will

have unique antecedents. It also reinforces the importance for GVT research to carefully

consider which facet(s) of performance the study is concerned with and to avoid

conceptualizing and operationalizing GVT performance as a global latent construct.

14

Second, the predictions for a negative relationship between personal diversity and GVT

effectiveness were based on two related theories: similarity-attraction theory (Berscheid &

Walster, 1969; Byrne, 1971) and social identity theory (Tajfel, 1974). The basic premise of

these theories suggest that people are attracted and drawn to other people who share similar

characteristics and beliefs. This theoretical perspective received general support as most

personal diversity measured were negatively related to GVT effectiveness. However, the

findings were not uniform for both facets of performance as personal diversity has a stronger

effect on psychological outcomes than on task outcomes. This provides new insights into these

theories, illustrating that the effects of personal similarities may be more related to personal

enjoyment and satisfaction, and less with actual objective performance.

Third, we make similar theoretical discoveries, but in the opposite direction, drawing

on the resource-based view. It suggests that bundling more heterogeneous resources (i.e.,

greater team diversity) should be more valuable and enhance team success. In our GVT context,

we find some support for this as the findings suggest that contextual diversity is largely

positively related to task outcomes. However, although objective supervisor-rated performance

may improve, it does not enhance participants’ satisfaction and enjoyment of the project. The

theoretical insights and particularly the differential effects as to what drives psychological

outcomes compared with task outcomes has major managerial implications.

5.2. Managerial Implications

Some managers might argue that he or she should only be concerned about the objective

performance of the GVT, and that employee satisfaction and enjoyment is beyond the

manager’s concerns. However, even though the correlation between psychological outcomes

and task outcomes was rather modest in this study, an overall relationship between job

satisfaction and job performance is well established. Thus, one would expect that, over time,

low personal satisfaction would lead to lower performance and job switching, which suggests

that managers would be best served being concerned about both task outcomes and

psychological outcomes.

Our findings provide some guidance to help managers maximize both. The optimal

team composition might be one where team members are similar in terms of their personal

characteristics, such as age, gender, skills and perceptions, but as different as possible in terms

of the characteristics of the societies they grew up in. This would allow to minimize

interpersonal problems, while maximizing the team’s tasks performance. Project scope may

also influence whether managers should be more concerned about psychological outcomes or

task outcomes. An emphasis on personal homogeneity may be more valuable on relatively

routine projects with fewer demands for creative thinking, and where employee satisfaction

and retention are important. In contrast, if the project requires complex problem solving and

addressing difficult international business challenges, a team comprised of individuals of

diverse backgrounds may achieve better results.

Our findings also reinforce that diversity is a very complex construct, and that managers

should be reminded to not think about it as a single factor. When it comes to the issue of

recruitment, selection, and team composition, it would be wrong to think about effectiveness

of “diverse” versus “homogeneous” teams in general. There are many different types of

diversity and many different types of team effectiveness and their relationships vary both in

terms of direction and magnitude. Although managers may prefer simple actionable advice,

our study reemphasizes that oversimplification may be counterproductive. At the very least, in

a discussion about “team diversity,” it is important to recognize that there are many types of

diversities and to not lump them all in a single pile.

15

It is also important to note that while the effects of the different types of team diversity

on the different facets of team effectiveness are statistically significant and practically

meaningful, team diversity still explains only a portion of variation in team effectiveness. In

fact, some of the effects reported in this study are rather limited, particularly the effects of

contextual diversity. We would like to warn managers that diversity is not a silver bullet that,

once present in a team, will automatically lead great problem solving (or conversely to conflicts

and interpersonal problems). It is a factor that clearly plays and role and must be taken into

account when forming and managing teams, but it is only one of many factors that affect team

effectives.

5.3. Limitations and Direction for Future Research

While the present study advances our understanding of the effects of diversity in GVTs

in a number of important ways, it cannot address all relevant issues and much work still

remains. For example, we evaluate a broad main effects model. Future research may want to

expand our work by considering non-linear relationships and/or a range of moderators and

mediators (Haas, 2010). For example, it is worth exploring the extent to which communication,

effort, conflicts, and other team dynamic factors mediate the relationship between team

diversity and effectiveness.

Second, we focused on team-level outcomes and averaged all constructs to the team

level. However, it is likely that individual team members had unique experiences. Thus, future

research may examine but individual-level outcomes, such as individual performance and

commitment.

Third, our study was based on a student sample. Because the cultural diversity and

geographic dispersion of our teams were real, the task involved a real consulting project for a

corporate client, and the work design and incentive structure were similar to a corporate project

environment, we do not see this as a major threat to the generalizability of our findings.

However, there may be another issue related to our sampling procedures that we could not

properly address in the present study. All participants were business students on their way to

join the global business elite. There is a growing body of literature that suggests that the values

and worldviews of the representatives of this global stratum may be relatively homogeneous

(Berger, 1997; Taras, Steel, & Kirkman, 2016). It is possible that the nature of our sample

masked some of these effects. In fact, the rise of the “global elites” may explain the relatively

weak effect of contextual diversity on team effectiveness observed in this study. As the

characteristics of the social circles and economic and institutional environments of a business

person in Brazil and a business person in in Germany are becoming more alike, the benefits

offered by contextual diversity may be shrinking. If the global business elites are indeed

becoming more alike, this important benefit of background diversity may be lost. It would be

fruitful to explore this notion in more depth, building on what we found in this study.

16

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22

7. Tables and Figures

Figure 1. The Effects of Different Types of Diversity on Team Effectiveness

Team Effectiveness

Task outcomes Psychological Outcomes

Diversity

Personal

(-) Weak negative effect

I.e. weaker effect size than

for psychological outcomes

(- -) Strong negative effect

Reasons:

• Communication

challenges

• Out-group prejudice

• Less similarity-attraction

Contextual

(++) Strong positive effect

Reasons:

• Variety of perspectives

• Variety of knowledge

pools and resources

• Interactive learning

(+) Weak positive effect

I.e. weaker effect size than

for task outcomes

(- -) and (-) indicate negative effects where (- -) denotes a relatively stronger negative

effect than (-);

(+ +) and (+) indicate positive effects where (+ +) denotes a relatively stronger positive

effect than (-)

23

Table 1. Complete Correlation Matrix

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

Team effectiveness

1. Task outcomes (0.85)

2. Psychological outcomes 0.24 (0.94)

Controls

3. Team size -0.09 0.01 (N/A)

4. Percent male -0.14 -0.10 0.10 (N/A)

5. Avg. team age 0.06 0.05 0.14 0.15 (N/A)

6. Avg. international experience 0.10 -0.03 -0.47 -0.07 -0.07 (N/A)

7. Avg. technical skills 0.19 0.59 -0.11 -0.10 -0.07 -0.04 (0.71’)

8. Avg. working language skills 0.15 0.50 -0.17 -0.08 -0.07 -0.03 0.81 (0.74’)

9 Avg. cultural intelligence 0.10 0.20 -0.27 -0.05 -0.10 0.14 0.19 0.15 (0.78)

Personal diversity

10. Age s 0.01 0.02 0.10 0.10 0.83 -0.09 -0.07 -0.07 -0.08 (N/A)

11. Gender s -0.11 -0.06 0.14 0.41 0.08 -0.07 -0.08 -0.08 -0.05 0.06 (N/A)

12. Working language skills s -0.12 -0.34 0.08 0.07 0.01 0.09 -0.60 -0.70 -0.13 0.05 0.10 (0.74’)

13. Technical skills d -0.11 -0.35 0.09 0.07 0.04 0.10 -0.67 -0.55 -0.16 0.06 0.06 0.79 (0.71’)

14. Personal values d -0.01 0.00 0.05 -0.03 0.02 0.07 -0.04 0.03 -0.02 0.00 0.04 0.00 0.04 (0.86)

15. Cultural intelligence d -0.02 -0.09 -0.05 0.01 -0.11 -0.06 -0.05 -0.06 0.11 -0.07 -0.02 0.05 0.06 0.04 (0.78)

16. Perceived differenced -0.17 -0.41 0.03 -0.04 -0.06 0.01 -0.35 -0.34 -0.17 -0.02 -0.02 0.23 0.23 0.06 0.04 (0.85)

Contextual diversity

17. National variety s 0.09 0.02 0.02 -0.10 0.03 0.15 0.02 0.00 0.03 0.01 0.03 -0.06 -0.05 0.05 -0.04 0.06

18. GDP/capita PPP 0.01 0.04 -0.26 -0.08 -0.07 -0.03 0.08 0.18 -0.03 -0.05 -0.06 0.01 0.02 0.04 0.08 0.08

19. HDI -0.04 0.00 0.10 -0.05 0.33 0.02 -0.17 -0.14 -0.18 0.26 -0.03 0.11 0.16 0.11 -0.13 0.24

20. GINI 0.02 -0.08 -0.09 0.02 -0.14 0.13 -0.03 -0.06 0.07 -0.06 0.05 0.07 -0.01 -0.05 0.00 -0.03

21. Corruption 0.06 0.03 -0.12 -0.05 0.02 0.02 0.05 0.11 -0.14 0.04 -0.03 0.04 0.05 0.09 -0.05 0.06

22. Civil freedom 0.01 0.09 -0.10 -0.18 -0.16 0.08 0.07 0.06 0.05 -0.15 -0.08 -0.09 -0.07 0.04 0.04 0.08

23. Percent women in parliament 0.10 0.03 0.26 0.02 0.11 -0.17 0.02 -0.04 -0.13 0.09 0.03 -0.02 -0.06 -0.01 -0.08 -0.04

24. Religiosity 0.08 -0.05 0.18 -0.09 0.09 0.06 -0.10 -0.13 -0.11 0.08 -0.08 0.02 0.05 0.08 -0.09 0.17

25. National cultural value distance 0.09 0.03 -0.17 -0.03 -0.07 0.10 0.09 0.10 0.08 -0.09 -0.03 -0.02 -0.02 -0.10 0.06 -0.03

26. Geographic dispersion -0.02 -0.02 -0.19 0.01 -0.23 0.18 -0.01 -0.02 0.18 -0.19 -0.03 -0.02 -0.05 -0.06 0.13 0.16

27. Time-zone dispersion (hours) 0.05 0.03 -0.10 -0.06 -0.24 0.02 0.04 0.02 0.05 -0.17 -0.09 -0.03 -0.05 0.00 0.07 0.12

24

Table 1. Complete Correlation Matrix (cont’d)

17 18 19 20 21 22 23 24 25 26

Contextual diversity

17. National variety s (N/A)

18. GDP/capita PPP 0.17 (N/A)

19. HDI 0.18 0.23 (N/A)

20. GINI 0.15 -0.17 -0.30 (N/A)

21. Corruption 0.16 0.69 0.35 0.00 (N/A)

22. Civil freedom 0.17 0.13 0.11 -0.19 0.23 (N/A)

23. Percent women in parliament 0.11 -0.14 0.05 0.33 0.22 -0.08 (N/A)

24. Religiosity 0.20 -0.15 0.37 -0.09 0.04 0.14 0.21 (N/A)

25. National cultural values 0.24 0.47 -0.14 0.12 0.34 0.09 0.08 0.08 (N/A)

26. Geographic dispersion 0.33 0.19 -0.03 0.12 0.02 0.10 -0.06 0.04 0.25 (N/A)

27. Time-zone dispersion (hours) 0.29 0.24 0.02 -0.01 0.15 0.14 -0.03 0.16 0.26 0.77

N=804, correlations over 0.06 are significant at p<0.05, correlations over 0.08 are significant at p<0.01

Internal reliabilities in parentheses, (N/A) indicates presumed perfect measurement reliability or measured with a single direct question and

Cronbach’s alpha non-applicable

‘ Indicates inter-rater reliability, rather than internal reliability, obtained based on single-item peer-evaluations rather than multi-item self-report

25

Table 2. Comparison of Bi-Variate Diversity Effects by the GVT Effectiveness Dimensions

Team Effectiveness Task outcomes Psychological

Outcomes Correlation

Difference

Significance Task outcomes

Psychological outcomes 0.240**

Controls

Team size -0.086** 0.010 *

Percent male -0.140** -0.104**

Avg. team age 0.061** 0.050

Avg. prior international experience 0.097** -0.030 **

Avg. technical skills 0.193** 0.593** **

Avg. working language skills 0.151** 0.503** **

Avg. cultural intelligence 0.103** 0.196** **

Personal Diversity

Age s 0.012 0.021

Gender s -0.113** -0.060* *

Working language skills s -0.116** -0.337** **

Technical skills d -0.106** -0.352** **

Personal values d -0.013 0.002

Cultural intelligence d -0.020 -0.086** *

Perceived differences d -0.170** -0.406** **

Contextual Diversity

National variety s 0.088** 0.024 **

GDP/capita, PPP 0.014 0.037

HDI 0.038 0.003

Gini 0.021 -0.084** **

Corruption 0.060** 0.031

Civil freedom 0.012 0.091** *

Percent women in parliament 0.104** 0.028

Religiosity 0.079** -0.053 **

National cultural values diversity,

Hofstede 0.087** 0.027 *

Geographic dispersion -0.019 -0.021

Time zone dispersion 0.050* 0.032

* p<0.05; ** p<0.01

N=804

26

Table 3. Multivariate Regression Results on Psychological Outcomes

Control Personal

Personal

+ Control Contextual

Contextual

+ Control Full

(1) (2) (3) (4) (5) (6)

Constant -0.031** 5.487** 1.118** -.125 -0.490 1.119**

Team size 0.060** 0.060** 0.068** 0.059**

Percent male -0.122** -0.134** -0.073 -0.097

Avg. team age 0.020** 0.017** 0.013** 0.006

Avg. intern ’l experience 0.011 0.012 0.016** 0.016

Avg. technical skills 0.493** 0.423** 0.497** 0.432**

Avg. language skills 0.131** 0.118* 0.124** 0.113

Avg. cultural intelligence 0.145** 0.128** 0.182** 0.151**

Personal Diversity

Age s 0.003 -0.004 -0.002

Gender s -0.040 0.003 -0.003

Working language skillss -0.177** 0.024 0.017

Technical skills d -0.216** 0.078 0.068

Personal values d 0.122 0.083 0.042

Cultural intelligence d -0.206** -0.181** -0.144**

Perceived differencesd -0.350** -0.224** -0.276**

Contextual Diversity

National variety s 0.251 0.426 0.201

GDP/capita PPP 0.000 0.000 0.000

HDI -0.126 -1.029** -1.544**

GINI -0.014** -0.003 -0.004

Corruption -0.036 -0.063 -0.079**

Civil freedom 0.085** 0.057** 0.066**

Percent women in

parliament 0.014** 0.007* 0.009**

Religiosity -0.007** -0.005 -0.005**

Cultural values 0.006 -0.001 0.000

Geographic dispersion 0.024** 0.000** 0.003

Time-zone dispersion (hrs) 0.031** 0.031** 0.020

R2 0.383** 0.247** 0.410** 0.031** 0.406** 0.443**

ΔR2 (compared to Control

model) -0.136** 0.027** -0.352** 0.023** 0.060**

27

Table 4. Multivariate Regression Results on Task outcomes

Control Personal

Personal

+ Control Contextual

Contextual

+ Control Full

(7) (8) (9) (10) (11) (12)

Constant 2.090** 6.407 2.386* 2.765** -0.093 0.391

Team size -0.014 0.031 -0.009** 0.062

Percent male -0.428** -0.441** -0.454** -0.529**

Avg. team age 0.042** 0.081** 0.040** 0.088**

Avg. intern ’l experience 0.035** -0.037 0.052** -0.013

Avg. technical skills 0.386** 0.554** 0.365** 0.503**

Avg. language skills -0.014 -0.199 -0.090 -0.232

Avg. cultural intelligence 0.133* 0.044 0.226** 0.157**

Personal Diversity

Age s 0.018 -0.036 -0.036*

Gender s -0.414** -0.267** -0.132

Working language skills s -0.111 -0.107 -0.109

Technical skills d -0.013 0.379** 0.318**

Personal values d -0.076 -0.068 -0.136

Cultural intelligence d -0.044 -0.036 0.042

Perceived differencesd -0.357** -0.282** -0.187**

Contextual Diversity

National variety s 2.102** 0.376 1.164*

GDP/capita PPP 0.000 0.000 0.000

HDI 1.969** 1.851** 2.734**

GINI -0.013 -0.018* -0.014

Corruption 0.089 0.136* 0.055

Civil freedom -0.066 -0.164** -0.109

Percent women in

parliament 0.024** 0.023** 0.029**

Religiosity 0.009** 0.007* 0.013**

Cultural values 0.010 0.007 0.005

Geographic dispersion 0.054** 0.065** -0.086**

Time-zone dispersion (hrs) 0.045* 0.070** 0.082**

R2 0.071** 0.041** 0.096** 0.037** 0.107** 0.148**

ΔR2 (compared to Control

model) -0.030** 0.025** -0.034** 0.036** 0.077**

28

Table 5. Correlations, Personal and Contextual Diversity Blocks 1 2 3 4 5 6

1. Task outcomes 1.00 0.28 -0.28 -1.00 0.14 0.12

2. Psychological outcomes 0.25 1.00 0.64 -0.61 0.01 0.03

3. Personal diversity block, all 7 variables -0.16 -0.38 1.00 1.00 0.02 -0.04

4. Personal diversity block, select 5 variables -0.18 -0.42 0.91 1.00 0.05 -0.02

5. Contextual diversity block, all 11 variables 0.10 0.01 0.01 0.03 1.00 1.00

6. Contextual diversity block, select 8 variables 0.09 0.02 -0.02 -0.01 0.89 1.00

N=804, correlations over 0.06 are significant at p<0.05, correlations over 0.08 are significant at p<0.01

The values above main diagonal (in italics) are corrected for unreliability attenuation

29

Table 6. Multivariate OLS Regression, Personal and Contextual Diversity Blocks

Task outcomes Psychological Outcomes

Control

Diversity

All

Diversity

Select

Full

All

Full

Select Control

Diversity

All

Diversity

Select

Full

All

Full

Select

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)

Constant 2.090** 4.832** 4.832** 2.283 2.395** -0.031** 3.906** 3.906** 0.162 0.239

Team size -0.014 -0.010 -0.003 0.060** 0.062** 0.064**

Percent male -0.428** -0.360** -0.364** -0.122** -0.105** -0.107**

Avg. team age 0.042** 0.048** 0.041** 0.020** 0.023** 0.019**

Avg. international

experience 0.035** 0.035** 0.039** 0.011 0.012* 0.013*

Avg. technical skills 0.386** 0.327** 0.332** 0.493** 0.440** 0.442**

Avg. working language

skills -0.014 -0.061 -0.062 0.131** 0.126** 0.129**

Avg. cultural intelligence 0.133* 0.143** 0.136* 0.145** 0.145** 0.141**

Personal Diversity -0.315** -0.271** -0.139** -0.094* -0.312** -0.276** -0.076** -0.052**

Contextual Diversity 0.107** 0.076** 0.042 0.035 0.018 0.014 0.015 0.005

R2 0.071** 0.040** 0.045** 0.075** 0.076** 0.383** 0.149** 0.180** 0.387** 0.386**

** p<0.05, * p<0.10, N=804

Minerva Access is the Institutional Repository of The University of Melbourne

Author/s:

Taras, V; Baak, D; Caprar, D; Dow, D; Froese, F; Jimenez, A; Magnusson, P

Title:

Diverse effects of diversity: Disaggregating effects of diversity in global virtual team

Date:

2019-12-01

Citation:

Taras, V., Baak, D., Caprar, D., Dow, D., Froese, F., Jimenez, A. & Magnusson, P. (2019).

Diverse effects of diversity: Disaggregating effects of diversity in global virtual team. Journal

of International Management, 25 (4), https://doi.org/10.1016/j.intman.2019.100689.

Persistent Link:

http://hdl.handle.net/11343/228895

File Description:

Accepted version